Brain-Computer Interface (BCI) research are emerging in the last few years providing non-invasive, wireless and low-cost ElectroEncephaloGraphy (EEG) devices. The increasing study of neurosciences and the need to respond to specific human brain diseases are two important factors to this evolution.
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Thursday, April 06, 2017

Matlab code to learn Recurrent Waveforms within EEGs

When experts analyze EEGs they look for landmarks in the traces corresponding to established waveform patterns, such as phasic events of particular frequency or morphology. This modeling approach automatically learns the waveforms corresponding to transient, reoccurring events within EEG traces.

The methodology is based on a sparsely excited model of a single EEG trace, and the model parameters are estimated using shift-invariant dictionary learning algorithms developed in the signal processing community. On the motor imagery dataset, linear discriminant analysis can distinguish the type of motor imagery based on the spatial patterns of a subset of the learned waveforms.